The Participation Divide Among Online Experts : Experience, Skills and Psychological Factors as Predictors of College Students Web Content Creation

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1 Journal of Computer-Mediated Communication The Participation Divide Among Online Experts : Experience, Skills and Psychological Factors as Predictors of College Students Web Content Creation Teresa Correa Doctoral student, College of Communication, University of Texas at Austin, This study explored factors that predict online content creation among college students. A Web-based survey revealed that there are differences by gender, race, and age even among this wired group. Drawing from literature on technology adoption, the digital divide, and self-determination theory, this study found that psychological factors perceived competence and both extrinsic and intrinsic motivations predict content creation. Among the experience variables, having a computer in the students own room is associated with content creation when controlling for all other factors. The gender divide disappears when experience, skills, perceived competence, and intrinsic motivation are considered. Finally, a new racial gap emerged; whites are less likely than minorities to participate in the Web even after controlling for all other variables. An earlier version of this paper received an award in the Jung-Sook Lee Student Paper Competition of the Communication and Technology Division, Association for Education in Journalism and Mass Communication annual conference, Boston, August, The author would like to thank Sebastián Valenzuela and Dr. Paula Poindexter for their helpful comments. doi: /j x Young adults, particularly college students, are considered online experts. Their age, education, and access to new technologies put them in a privileged position within the digital world (Fox & Madden, 2005; Hargittai & Hinnant, 2008; Madden, 2003; Van Dijk, 2005). However, having access to the Internet is different from having access to the content that resides on it (Newhagen & Bucy, 2004). Research has found there is a divide by gender and socioeconomic status both in frequency and breadth of activities that require active involvement and greater technological skills among young people and college students (Hargittai & Walejko, 2008; Livingstone & Helsper, 2007; Peter & Valkenburg, 2006). In the beginning, the literature conceptualized the digital divide as a gap between those who do and those who do not have access to digital technologies (e.g., Hoffman & Novak, 1998; Katz & Aspden, 1997; van Dijk, 2006). However, as more people are Journal of Computer-Mediated Communication 16 (2010) International Communication Association 71

2 using the Web to communicate, retrieve information, and contribute with content, academics and policy makers are shifting the focus from a simplistic and binary conceptualization of Internet access to a more advanced and complex approach that involves width and depth of Internet usage (Dholakia, Dholakia, & Kshetri, 2004; Livingstone & Helsper, 2007; van Dijk, 2006). This study addresses this shift in focus by exploring the factors that shape participation in the Web, defined as online content creation, among a highly wired group, such as college students. This is a highly Internet-connected group: The segment 18 to 24 years old who are in school is the most connected group among web users (86.7% wereonline) (NTIA, 2004). Additionally, teens (12 17) and young adults (18 32) are the most likely groups to participate online by creating blogs, using social network sites, and sending and receiving instant messages (Jones & Fox, 2009). Therefore, inequalities regarding Internet access or basic Web use should not be relevant among this group. However, differentiated uses of more complex activities such as engaging in the interactive dimensions of the digital media may be prevalent. Some of these interactive dimensions involve creation of online content such as creating blogs, posting comments on blogs and on news websites, using social network sites such as Facebook, uploading videos or photos, and contributing content for citizen journalism sites (Madden & Fox, 2006). In the increasingly usergenerated Web, a divide in the breadth and frequency of participation might lead to the emergence of a system that will be dominated by contributors while the rest will remain mere consumers of content (Hargittai & Walejko, 2008). Furthermore, Livingstone (2004, p. 11) argued that content creation is crucial for the democratic agenda because users become not merely consumers but also citizens. Most of the literature has focused on sociodemographics to predict the digital divide (e.g., Bucy, 2000; Hoffman & Novak, 1998; Howard, Rainie, & Jones, 2001; Katz & Aspden, 1997; Loges & Jung, 2001). It is necessary, however, to add additional layers of predictors that shed light on reasons that may explain the divide. By relying on the research on the digital divide, technology adoption, and selfdetermination theory, this study explores the role of Internet experience, online skills, and psychological factors in the participation divide in the Web. Specifically, it investigates whether experience with computers and the Internet and general online skills play a role in web content creation. Additionally, it uses self-determination theory to explore to what extent psychological predictors such as perceived competence, extrinsic motivation and intrinsic motivation are related to posting material on the Web. Finally, it examines whether any of these predictors intervene in the relationship between sociodemographic characteristics and content creation to explore what are the reasons behind the participation divide. Literature Review Research on the digital divide has been moving beyond simple adoption and access to a multifaceted concept that involves cognitive access and width and depth in 72 Journal of Computer-Mediated Communication 16 (2010) International Communication Association

3 adoption and use of technologies such as the Internet (e.g., de Haan, 2004; Dholakia, Dholakia, & Kshetri, 2004; Hargittai, 2002; Ram & Jung, 1994; Shih & Venkatesh, 2004; van Dijk, 2006). Cognitive access refers to individual psychological resources used to access the technology. Width of adoption is defined as the number of different uses of the product and depth of adoption as the amount of usage (Gatignon & Roberston, 1991). The emergence of this second wave of research has been called use-diffusion model (Shih & Venkatesh, 2004), usage gap (van Dijk, 2004), second-level digital divide (Hargittai, 2002), and emerging digital differentiation (Peter & Valkenburg, 2006). Sociodemographic predictors of the usage gap Scholars have found significant differences by gender, socioeconomic status, race and age in more complex Internet activities among teens, young adults, and college students. Regarding gender, the uses of the participatory Web among young people is differentiated. For example, while teenage girls tend to be more avid bloggers, boys are more likely to upload videos (Lenhart, Madden, Rankin Mcgill, & Smith, 2007). Similarly, while there is no gender difference among college students in social network site use, males are more likely than females to use video-sharing applications (Chen, 2007). In addition, women college students were less likely to post material on the Web (Hargittai & Walejko, 2008). As for socioeconomic status (measured by parental schooling), the authors found that college students that are better off are more likely to engage in creative activities than those that are less well off. Regarding race, college students online experiences are differentiated. Regarding content creation, Jones et al. (2009) found blacks were more likely to keep a blog than whites and Hispanics. In contrast, Hispanics were more likely to share files than whites and blacks. Other recent studies have found that among Internet users, white adults are less likely than minorities to use online social media (Correa, Willard Hinsley, & Gil de Zuniga, 2010; Lenhart, 2009). As for age, teens (12 17 years old) and young adults (18 32 years old) are more likely than older generations to create content (Jones & Fox, 2009). Even within the young adult cohort, age makes a difference. Students from 18 to 21 years old are significantly more likely than those 22 and older to use social network sites (Chen, 2007). Experience, skills and the usage gap The ever-increasing opportunities offered by the Web require higher levels of experience and skills to take advantage of communication technologies. Years of Internet use is a powerful predictor of greater and more complex Web usage (e.g., Eastin & LaRose, 2000; Livingstone & Helsper, 2007; Peter & Valkenburg, 2006). Also, the more places that people have access to the Web the greater their Internet mastery and usage (Hargittai & Hinnant, 2008). Home access would be the place that has the greatest impact on digital mastery because it offers autonomy and allows learning through experimentation (de Haan, 2004; Hargittai & Hinnant, 2008; Livingstone Journal of Computer-Mediated Communication 16 (2010) International Communication Association 73

4 & Helsper, 2007). Further, the presence of computers in children s own rooms is a strong predictor of their digital mastery (de Haan, 2004). Regarding skills, most of the extant research has focused on self-reported skill levels (van Dijk, 2006). Using self-reported abilities, Livingstone and Helsper (2007) revealed that skills have the greatest impact on number of activities performed in the Web. Using a test to measure skills in a controlled setting, Hargittai (2002) found that age is inversely associated with people s level of online skills. Also, men and women do not differ greatly in their actual skills, although females perceive themselves as less competent, which may affect their online behavior (Hargittai & Shafer, 2006). Further, Hargittai and Walejko (2008) found that the gender divide in sharing files disappeared at equal levels of skills. Psychological predictors of the usage gap Two psychological variables have been deemed important predictors of technology adoption and usage behavior: self-efficacy and motivation. Self-efficacy. Developed by Bandura (1986), self-efficacy is a form of self-evaluation that influences people s decisions on what they can do with a given skill. Specifically, Internet self-efficacy is the individuals beliefs in their capacity to perform certain Internet actions in order to produce a given goal (Eastin & LaRose, 2000). Studies have found Internet self-efficacy is the stronger predictor of frequency of internet usage among adolescents, especially among male teens (Broos & Roe, 2006), and number of activities performed in the Web (Livingstone & Helsper, 2007). Motivation. Most of the literature on motivation on Internet use has been measured from a functional uses-and-gratifications approach (e.g., Cho, Gil de Zuniga, Rojas, & Shah, 2003; Papacharissi & Rubin, 2000; Sun, Rubin, & Haridakis, 2008). That is, studies described the goals and reasons why people use certain applications of the web. In the literature on technology adoption, the widely used technology acceptance model emphasizes functional or extrinsic motivational factors such as perceived usefulness of the technology (Davis, 1989; Lee, Cheung, & Chen, 2005). A few studies using this model have included intrinsic motivators (defined as perceived enjoyment and satisfaction) to predict acceptanceand usageofnew technologies such as computers and the Internet. They have consistently found that both extrinsic and intrinsic motivations are important predictors of technology usage intentions and behavior (Davis, Bagozzi, & Warshaw, 1992; Lee, Cheung, & Chen, 2005; Teo, Lim, & Lai, 1999; Venkatesh, 1999). These studies reveal that the educational divides in Internet usage were fully or partially mediated by motivational variables (Teo, 2001; Teo, Lim, & Lai, 1999), suggesting that at equal levels of motivation, the educational differences in Internet usage wane. Psychological predictors and Self-Determination theory To date, research on technology adoption, the digital divide and internet usage has investigated the effects of different psychological predictors using distinct theoretical 74 Journal of Computer-Mediated Communication 16 (2010) International Communication Association

5 approaches (e.g., Broos & Roe, 2006; Davis, Bagozzi, & Warshaw, 1992; Hargittai & Hinnant, 2008; Livingstone & Helsper, 2007; Teo, 2001). By using self-determination theory (Deci & Ryan, 1985), this article seeks to integrate different psychological concepts into a single framework to predict participation in the Web. Self-determination theory, widely used in the field of educational psychology, explores the role of both extrinsic and intrinsic motivation for learning and performance. To date, most of the research on motivations to use Internet has been measured from a uses-and-gratifications approach. Although this approach is useful to describe the various goals users may seek to attain when using certain technologies, it is important to take into account to what extent people feel moved to use certain Internet applications. This theory proposes there are different components of motivation depending on the degree to which motivations are self-determined (i.e., emanate from the self) (Ryan & Deci, 2000a). Intrinsic motivation, defined as doing something because it is inherently interesting and enjoyable, is the prototypic instance of self-determination. Extrinsic motivations are more instrumental; they refer to doing something not for its own sake but because of a separate outcome. There are different degrees of self-determination. Some extrinsic motivations are external (i.e., people do something for external pressures or rewards) and some are internal or self-determined (people do something because they internally accepted the value or utility of atask). Studies have found that perceived competence (also called self-efficacy), which means feeling able to perform a task regardless of the actual skill, is a key psychological factor that determines higher levels of self-determined motivation (e.g., Bandura & Cervone, 1983; Deci & Ryan, 1985; Harackiewicz & Larson, 1986; Losier & Vallerand, 1994). It has been found that the influence of perceived competence on selfdetermined motivations is stronger than the reverse relationship (Goudas, Biddle, & Martin, 1995; Losier & Vallerand, 1994; Vallerand & Reid, 1984). Motivations that have greater levels of self-determination are crucial for having better performance (Miserandino, 1996) as well as persistence and enhanced learning (Pelletier, Tuson, & Haddad, 1997). Regarding technology, Ryan and colleagues (2006) found that perceived competence predicts intrinsic motivation of video gaming and, subsequently, future game play. Although the technology adoption literature has found that both extrinsic and intrinsic motivations predict Internet usage (e.g., Davis, Bagozzi, & Warshaw, 1992; Teo, 2001; Teo, Lim, & Lai, 1999; Venkatesh, 1999), these studies have not included other factors that affect Internet usage such as experience, skills, and perceived competence. Based on the literature on technology adoption, the digital divide, and selfdetermination theory, this study examines the following research questions and hypotheses: RQ1: What is the relationship between students sociodemographic variables gender, race, socioeconomic status, and age and online content creation? Journal of Computer-Mediated Communication 16 (2010) International Communication Association 75

6 RQ2: What is the relationship between students sociodemographic variables and Internet experience and skills? RQ3: What is the relationship between students sociodemographic variables and three psychological factors as they relate to content creation: content creation perceived competence, extrinsic and intrinsic motivation to create content? H1: Higher levels of Internet experience will be associated with greater levels of online content creation. H2: Higher levels of general online skills will be associated with greater levels of online content creation. H3: Higher levels of perceived competence will be associated with greater levels of online content creation. H4: Higher levels of both extrinsic and intrinsic motivation will be associated with greater levels of online content creation. RQ4: Which variables Internet experience, general online skills, content creation perceived competence, and both extrinsic and intrinsic motivation to create content mediate the relationship between gender, race, socioeconomic status and age and online content creation? Method Procedure To answer the research questions and test the hypotheses, a Web-based survey was conducted among college students at two large public universities in the Southwestern United States. Survey respondents were contacted by . The addresses were obtained through a Public Information Act request. College students are the appropriate population for exploring the factors that affect content creation because they are a highly wired group. Thus, it is more likely to find variance in the frequency and breadth of participatory activities. Using a systematic random sample with a random starting point, 18,168 students addresses were selected from both universities. 1 In February 2009, an invitation to participate in a survey on Internet uses was ed to the students. Variables description To examine the respondents sociodemographic characteristics, standard demographic questions were used. Students were asked their gender and race. 2 For the statistical analyses, race was recoded into five categories: 1) white, 2) black, 3) Hispanic, 4) Asian, and 5) Other race. Consistent with previous research on the digital divide (Hargittai & Walejko, 2008) and elsewhere (e.g., Carlson, Uppal, & Prosser, 2000; Elo & Preston, 1996), socioeconomic status was measured by parental schooling, which included both father and mother s level of education. 3 Respondents age was asked in an open-ended question. 76 Journal of Computer-Mediated Communication 16 (2010) International Communication Association

7 Online content creation was measured by an additive scale created with 10 activities. The selection of these activities was based on the Pew and Internet Life Project s definition of user-generated content (Madden & Fox, 2006) and a study on web content creation conducted by Hargittai and Walejko (2008). 4 To create the index, the responses of the different questions were first standardized and then combined into an additive scale that ranged from 0 to Internet experience was measured using four items that predict digital mastery: 6 frequency of usage, formal technology education, presence of computers in the home in which they grew up, and presence of computers in their own room in the house in which they were raised. 7 General online skills were measured by a scale developed for surveys by Hargittai (2005, 2008) based on controlled tests. This scale aggregates responses to five questions about respondents knowledge of computer- and Internet-related terms. 8 Because these items have high correlation with people s actual skills, they are better proxies of actual users skills than perceived abilities (Hargittai, 2005). Although in this study this construct is called skills, it is a measure of self-reported knowledge. The concepts perceived competence, extrinsic and intrinsic motivation regarding online content creation were measured by additive scales constructed with three items adapted from the self-determination theory literature (Self-determination theory, n.d.). 9 Extrinsic motivation was measured with the subscale that represents selfdetermined or internalized extrinsic motivations. Intrinsic motivation was measured with the interest/enjoyment subscale, which is the only one that addresses intrinsic motivation per se (Deci, Eghrari, Patrick, & Leone, 1994; Ryan, 1982). 10 Results Overview of sample A total of 3,139 students completed the survey, yielding a response rate of 17%, which falls within an acceptable rate for Web-based surveys of college students. Further, scholars have demonstrated that low response rates do not necessarily yield poorer quality data (Dillman, 2007). Of the total respondents, 41% were men and 59% were women. The mean age was 24 (SD = 6.9) (for a detailed description of the sample and Cronbach s alphas of the scales, see Appendix). A comparison of the sample s demographic breakdown against the demographic profile of the universities revealed that the results can be roughly generalized to the universities population because the distribution is quite similar. 11 Most of the variables were normally distributed and contained no extreme outliers. 12 The only exceptions were online content creation and frequency of usage, which were recoded as previously described. Is there a participation divide among college students? RQ1 asked about the relationship between students sociodemographic variables and online content creation. The results suggest that there is a digital participation divide Journal of Computer-Mediated Communication 16 (2010) International Communication Association 77

8 for gender, race, and age. As Table 1 shows, men were somewhat more likely than womentocreateonlinecontent. 13 Regarding race, whites were significantly less likely than African Americans, Hispanics and Asians to create content in the Web. 14 Asians were more likely to create content than any other group (see Table 2). Finally, age was inversely related to online content creation (see correlations in Table 3). There was no relationship between respondents SES and content creation (see Table 3). RQ2 asked about the association between the students sociodemographic variables and Internet experience and general online skills. Men were more likely than women to have had more technology classes, more computers in the home in which they grew up, and a PC in their own room (see Table 1). Regarding race and ethnicity, Asians were more likely to have had more computers in the house in which they grew up and a computer in their own room compared to the other groups. Whites used the Internet less frequently compared to Asians and blacks (see Table 2). Students from highersesweresignificantlymorelikelytohavemorecomputersinthehomeinwhich Table 1 Differences by Gender on Content Creation, Psychological Factors, Skills, and Experience Variables Male M (SD) Female M (SD) T statistic Online content creation (0 to 5.5) Psychological factors Perceived competence (1 to 19) Extrinsic motivation (1 to 19) Intrinsic motivation (1 to 19) Internet skills (1 to 31) Experience Number of technology classes (0 to 4) Number of home computers (0 to 4) Frequency of usage (minutes) Computer in own room (Yes) 2.57 (1.37) 8.93 (4.99) 7.93 (4.79) 9.98 (4.51) (7.42) 2.19 (1.42) 1.81 (1.12) (143.84) Note: Statistical analyses are based on t-tests and chi square. p <.05; p <.01; p < (1.18) 8.22 (5.07) 8.66 (4.78) 9.78 (4.54) (7.86) 2.00 (1.31) 1.66 (1.13) (143.93) % % X Journal of Computer-Mediated Communication 16 (2010) International Communication Association

9 Table 2 Differences by Race and Ethnicity on Content Creation, Psychological Factors, Skills, and Experience Variables White M(SD) Black M(SD) Hispanic M(SD) Asian M(SD) Other M(SD) F Online content creation (0 to 5.5) Psychological Factors Perceived competence (1 to 19) Extrinsic motivation (1 to 19) Intrinsic Motivation (1 to 19) Internet Skills (1 to 31) Experience Technology classes (0 to 4) Number of home computers (0 to 4) Frequency of usage (minutes) Computer in own room (Yes) 2.32 a (1.13) 8.10 a (5.02) 7.83 a (4.58) 9.60 a (4.43) a (7.83) 1.99 ac (1.33) 1.77 a (1.13) 117 a (176) 2.66 b (1.42) 8.74 ab (5.03) 8.31 a (4.99) 9.61 a (4.37) a (8.08) 2.11 ab (1.42) 1.37 b (1.16) 152 b (189) 2.60 b (1.33) 8.48 a (5.07) 8.14 a (4.93) 9.57 a (4.85) ab (8.01) 2.28 b (1.34) 1.37 b (.97) 127 abc (177) 3.02 c (1.40) 9.67 b (4.84) b (4.82) b (4.49) b (7.52) 2.25 bc (1.39) 2.01 d (1.16) 152 bc (257) 2.40 a (1.27) 8.88 ab (5.40) 8.22 a (4.85) ab (4.37) ab (8.71) 1.93 ac (1.40) 1.74 ad (1.10) 127 abc (169) % % % % % X Note: Statistical analyses are based on Anovas and chi square. Means that do not share letters (superscripts) are significantly different from each other at p <.05 by Tukey s posthoc tests. p <.05; p <.01; p <.001. they grew up. As for age, younger students were significantly more likely than older students to have been raised in a house with more computers and a computer in their own rooms. Older students tended to use the Web more frequently and were somewhat more likely to have taken technology classes than younger people (see Table 3). The second part of RQ2 asked about the connection between sociodemographic characteristics and general online skills. As Table 1 demonstrates, the largest difference was in gender: Men were significantly more likely than women to have online skills. As for race, whites, blacks had significantly less online skills than Asians. There was no difference between Hispanics and Asians (see Table 2). Table 3 also shows that older students were somewhat more likely than younger respondents to know about computer-related terms, although the relationship was weak. Finally, there was no connection between SES and Internet skills. Journal of Computer-Mediated Communication 16 (2010) International Communication Association 79

10 Table 3 Correlations among SES, Age, and Online Content Creation, Psychological Factors, Experience, and Skills Variables SES (parental education 1 to 12) 2. Age (continuous variable) 3. Perceived competence (1 to 19) 4. Extrinsic motivation (1 to 19) 5. Intrinsic motivation (1 to 19) 6. Technology classes (0 to 4) 7. Number of home computers (0 to 4) 8. Computer in own room (Yes = 1; No = 0) 9. Frequency of use (minutes) 10. Internet skills (1 to 35) 11. Online content creation (standardized scores) Note: Pearson correlations were used in all the statistical analyses, except from computer in own room. For this dichotomous variable, kendaull b correlation was applied. p <.05; p <.01; p < Journal of Computer-Mediated Communication 16 (2010) International Communication Association

11 RQ3 asked about the correlation between the students sociodemographic variables and the psychological factors content creation perceived competence, extrinsic and intrinsic motivation to create content. Men were more likely than women to perceive themselves as competent in creating content (see Table 1). Asians also perceived themselves as more competent than the other groups (see Table 2). Finally, younger respondents felt more competent than older students (see Table 3). There was no relationship for SES and content creation. As for extrinsic motivation to create content, females were more extrinsically motivated than men (see Table 1). Asians had significantly higher levels of extrinsic motivations than the other groups (see Table 2). Younger students were more extrinsically motivated. There was no relationship between SES and extrinsic motivation (see Table 3). Regarding intrinsic motivation, Asians were more intrinsically motivated than the other groups to create content (see Table 2). Younger students were slightly more intrinsically motivated than older students (see Table 3). There was no significant difference by gender and SES on intrinsic motivation. Experience, skills, and psychological factors as predictors of online participation H1 predicted that higher levels of Internet experience will be associated with greater levels of content creation. This hypothesis was partially supported. Controlling for sociodemographic variables, two out of the four items significantly predicted online content creation: Having more formal education on technology and having a computer in the room (see model 2 of Table 4). When all the variables were included in the analysis, only having a computer in the students own room remained a significant predictor of content creation (see model 4 of Table 4). H2 predicted that general online skills will be associated with greater levels of content creation. This hypothesis was partially supported. Controlling for sociodemographics and Internet experience, general online skills predicted content creation (see model 3 of Table 4). However, when the psychological variables were included in the model, this variable disappeared as a significant predictor (see models 4 and 5 of Table 4). Regarding the psychological predictors of content creation, H3 posed that higher level of perceived competence will be associated with greater levels of content creation. This hypothesis was supported. As both models 4 and 5 of Table 4 reveal, feeling able to create content was an important predictor of engaging in more complex activities in the Internet even when controlling for all the variables. It is also worth noting that perceived competence predicts content creation both directly and by affecting both extrinsic and intrinsic motivations, as self-determination theory poses. A comparison between model 4 and 5 of table 4 suggests that the strength of the relationship between perceived competence and content creation decreases considerably when the motivational variables are included in the model, suggesting that motivation is partially mediating the relationship between perceived competence and content creation. H4 predicted that higher levels of both extrinsic and intrinsic motivation will be associated with greater levels of content creation. This hypothesis was supported. Journal of Computer-Mediated Communication 16 (2010) International Communication Association 81

12 Table 4 Hierarchical Regression: Factors Contributing to Online Content Creation Model 1 Model 2 Model 3 Model 4 Model 5 Gender (male) Race Blacks Hispanics Asian Other race SES Age R 2 6.4% Internet experience Technology classes Number of home computers Computer in own room (yes) Frequency of usage R 2 incremental 1.9% R 2 total 8.1% General online skills R 2 incremental 1.4% R 2 total 9.5% Psychological predictors Content creation perceived competence R 2 incremental 6.5% R 2 total 16.0% Extrinsic motivation to create content Intrinsic motivation to create content R 2 Incremental 12.5% R 2 Total 28.5% p <.05; p <.01; p <.001. N = 2,371. Female and whites are reference categories. Cell entries represent standardized betas Journal of Computer-Mediated Communication 16 (2010) International Communication Association

13 As model 5 of table 4 shows, both types of motivation to create content (i.e., being motivated for a separable outcome such as utility and being motivated for interest and enjoyment) are important predictors of digital content creation. In sum, having a computer in the students own room as well as feeling competent and motivated to create content are the most important predictors of content creation (see model 5 of Table 4). Finally, RQ4 asked which of these variables intervened in the relationship between the sociodemographics and content creation. The results revealed that Internet experience, general online skills, perceived competence, and intrinsic motivation fully intervened in the relationship between gender and online content creation. The slight but significant gender divide that is shown in the baseline model vanished when the aforementioned variables were included in the model (compare the baseline model to models 1, 2, 3, and 5 in Table 4). Extrinsic motivation was not a factor behind the gender gap. On the contrary, women assigned greater value to content creation than men (see Table 1). This fact explains why the gender difference becomes significant when extrinsic motivation is included in the analyses (see model 5 of Table 4 and model 4 of Table 5). None of the variables fully mediated in the relationships between the racial and generational gaps and content creation, suggesting that there are other factors that may explain these divides. Discussion and Conclusion This study explored the factors that predict participation in the Web, defined as online content creation, among a highly wired group, such as college students. Specifically, it examined whether there is a divide by gender, race, socioeconomic status and age. It also investigated to what extent web experience, online skills, and psychological predictors of content creation affect digital content creation, and whether these factors intervene in the relationship between people s sociodemographic characteristics and content creation. Consistent with the recent literature that pays attention to more subtle digital inequalities, such as gaps in activities that require active involvement and greater technological skills, this study found that there are differences by gender, race and age in content creation even among this connected group. Regarding gender, men are somewhat more likely than women to create online content. Fortunately, these gender differences are small, although they remain significant after controlling for other sociodemographic variables. This study also found that this gender gap disappears when Internet experience, online skills, perceived competence to create content, and intrinsic motivation are taken into consideration. This result suggests that female students lower experience with the Web as well as their lower skills is associated with this slight gender gap. Regarding experience, having a computer in the students own room in the house in which they grew up was an important predictor of content creation (de Haan, 2004). Because women were less likely than men to be raised with a computer in their own room, they may have had fewer opportunities to self-experiment with technologies. Journal of Computer-Mediated Communication 16 (2010) International Communication Association 83

14 Table 5 Hierarchical Regressions: Factors Intervening Between Demographics and Content Creation Baseline Model Model 1 Model 2 Model 3 Model 4 Model 5 Gender (male) Race Blacks Hispanics Asian Other race SES Age R 2 6.7% Web Experience Technology classes.06 Number of home.02 computers Computer in own.10 room Frequency of usage.04 General online skills.15 Psychological.31 predictors Content creation perceived competence Extrinsic motivation.41 to create content Intrinsic motivation.41 to create content R 2 Incremental 1.9% 2.1% 9.6% 15.6% 16.4% R 2 Total 6.7% 8.4% 8.8% 16.3% 22.3% 23.1% p <.05 p <.01 p <.001 = variables that were not included in the regression. Female and whites are reference categories. Cell entries represent standardized betas. The sample sizes range from 2,469 to 2,489. Due to the slightly different sample sizes, the R 2 for the sociodemographic block varied 6.5% to 6.7% in the different models. To facilitate the interpretation of the table, the R 2 of the baseline model was used to calculate the incremental R 2 of the subsequent models. 84 Journal of Computer-Mediated Communication 16 (2010) International Communication Association

15 Psychological factors such as lower perceived confidence and intrinsic motivation (i.e., interest and enjoyment) regarding content creation are behind the gender differences. This finding is in line with previous studies, which have found that female college students feel less comfortable with computers than males (Cooper, 2006; Hargittai & Shafer, 2006). Additionally, consistent with self-determination theory literature, the results also showed that perceived competence catalyzes selfdetermined motivations. This may explain why intrinsic motivation was also a factor behind the slight gender gap. It is noteworthy that females had greater levels of extrinsic motivation than males, suggesting that despite they do not feel as competent as men in creating content, they found it more useful (see Table 1). This fact explains why there are gender differences in content creation when extrinsic motivation is taken into account in the models (see model 5 of Table 4 and model 4 of Table 5). Future research should pay attention to this finding because motivation researchers have been found that when people perceive something as challenging and important but they think their skill levels are low, they get anxious, which negatively affects their performance (Csikszentmihalyi, 1991). These results suggest that efforts to include women in the digital world should focus on increasing opportunities for self-experimentation and boosting self-confidence related to using new technology. Another finding that merits attention is the emergence of a racial divide in online content creation, although contrary to the trend in which minority groups are in a disadvantaged position. In this case, white students were less likely than minority groups to create online content even after controlling for all the remaining variables. Further analyses revealed that among all racial groups, white students created online content less frequently in 8 out of 10 activities. This finding is consistent with recent surveys conducted among American adults who use the Internet. It has been found that minorities are more likely to use applications of the Web 2.0 than whites (Correa, Willard Hinsley, & Gil de Zuniga, 2010; Jones et al., 2009; Lenhart, 2009). Also, African American teens were found to be more likely than whites to civically and politically participate in online settings (Harp, Bachman, Loke, & Cantrell, forthcoming). Perhaps, minority groups that already acquired material and cognitive access to the Internet are taking advantage of tools that provide new venues for participation and communication. Future research should explore whether there are cultural or other psychological factors playing a role in this phenomenon. The generational gap found in this study was expected. Younger students had more opportunities to experiment with computers they were more likely than older students to have more computers and a PC in their own room in the house in which they were raised. Therefore, it is expected they will have higher levels of skills, perceived competence, and both extrinsic and intrinsic motivations. It is possible that this study did not find digital differences by socioeconomic status because it measures SES only with parental schooling. Future research should develop a richer measure of SES. Internet experience was positively correlated with online content creation. Interestingly, having a computer in the students own room in the house in which Journal of Computer-Mediated Communication 16 (2010) International Communication Association 85

16 they were raised was the only factor that remained a significant predictor of digital participation when controlling for all other variables. As de Haan suggested (2004), these results demonstrate that self-experimentation is a significant step toward the digital inclusion process. Skills were positively associated with contribution of Web content, as previous studies suggest (Hargittai & Hinnant, 2008; Hargittai & Walejko, 2008). However, when perceived competence regarding content creation was included in the analysis, general online skills were not important anymore. This result suggests that competence perceptions regarding specific tasks may override the influence of actual skills in those tasks. This investigation used self-determination theory as a theoretical framework to integrate different psychological factors that are associated with digital mastery, such as content creation. This study found that psychological factors were the most important predictors of content creation (see Table 4). In other words, feeling competent to post material (text, videos, photos, and music) as well as feeling inspired to create content because it is inherently interesting and enjoyable (i.e, intrinsic motivation) as well as because it is useful and valuable (i.e, extrinsic motivation) are crucial steps to jump into the user-generated Web. It is also noteworthy that perceived competence predicts content creation both directly and by affecting motivation, which ultimately influences content creation. As self-determination theory poses, this study showed that when students feel more competent they become more motivated to create content. The data collection was conducted through a Web-based survey, which may be biased against students who spend less time on the Internet. However, this study focused on more complex Internet activities such as online content creation. Therefore, one could argue that the results and the divides may be even greater if the data collection had been conducted offline. Finally, because this is a cross-sectional research study; it is not possible to make causal inferences. Future research should conduct a panel study to make causal inferences about which factors influence digital mastery, especially online content creation. Notes 1 One of the universities was oversampled due to its greater sociodemographic diversity. While 35 percent (10,500) of the students from the university located in a metropolitan area were selected in the sample; 20 percent of the small town university s students were chosen (7,696). 2 Race and ethnicity had the following categories: African American or black; Caucasian or white; Hispanic or Latino; Asian; Pacific Islander; Native American or Alaskan native; Mixed race, and Other. 3 Parental education was preferred as a measure of socioeconomic status than income because many students do not receive financial aid from their parents. Thus, their 86 Journal of Computer-Mediated Communication 16 (2010) International Communication Association

17 current financial situation does not reflect their social background and resources with which they grew up. 4 Respondents were asked: Please tell me how often you do any of the following when you go online: 1) Instant messaging; 2) Post comments on blogs; 3) Participate in chat rooms or online discussions; 4) Use a social network site such as Facebook or MySpace; 5) Post comments in news websites; 6) Upload videos created by yourself; 7) Upload your own photos; 8) Post remixed (music, video, photo, text) from other people s material; and 9) Contribute with community news. For the first five items, the response categories were: Several times a day, About once a day, Nearly every day, 3 4 days a week, 1 2 days a week, Every few weeks, Less often, Never or seldom. For the remaining four items, the response choices were the following: Several times a week, About once a week, Every few weeks, About once a month, Less Often, Never or seldom. The remaining question included in the scale was: Have you ever created a blog? (Yes = 1; No = 2). 5 The content creation scale ranged from 0 to 10. Because the distribution was positively skewed, those who scored 2 standard deviations from the mean (scores above 5.50) were recoded as having a score of They represented 4.9% of the sample. As a result, the online content creation index runs from 0 to Years of experience using the Web was dropped as a measure of experience because 35% of the sample did not answer the question how old were you when you first started using the Internet. In any case, the analyses with the cases available showed that this variable did not predict content creation after controlling for demographics probably because the sample was very homogeneous in terms of years of experience (M = 10.3 years, SD = 3.1). 7 For frequency of usage, in an open-ended question, respondents were asked how much time they spend online on a typical day. Because frequency of usage was positively skewed, this variable was recoded. Those who scored two standard deviations above the mean (scores above 510 minutes per day) were recoded as spending 510 minutes per day on the Internet. They represented 3% of the sample. For formal technology education, respondents were asked: How many formal computer, software, and/or Web application classes have you taken? For presence of computers, they were asked how many computers did you have in the home in which you grew up? The response choices for both questions were 0, 1, 2, 3, more than 3. For presence in their room, they were asked: Did you have a computer in your own room in the home in which you grew up? Response choices: (1) Yes, (2) No. Although these four variables could have been collapsed into one construct called experience, it seemed pertinent to leave them separated for educational and public policy implications. 8 On a 7-point Likert scale, respondents were asked to rate their understanding of the following terms: jpg, frames, preference settings, newsgroups, pdf. 9 On a 7-point Likert scale, where 1 meant Not true at all and 7 Very true, students were asked about their feelings toward different activities they can do to participate in the Internet by creating online content, such as posting on blogs, posting photos or videos, contributing with news, using social network sites. The statements that reflected perceived competence were the following: 1) You re pretty skilled at creating online content; 2) You do pretty well at creating online content compared to other students; 3) You re not very good at online content creation. Journal of Computer-Mediated Communication 16 (2010) International Communication Association 87

18 10 Extrinsic and intrinsic motivations to create content were measured using the same 7-point Likert scale question that addressed perceived competence. The items that measured extrinsic motivation were the following: 1) You participate online because it is a good way to improve your social skills; 2) You participate online because it s exciting to try new ways of self-expression; 3) You believe participating online will help you connect with people. The statements that reflected intrinsic motivation were the following: 1) You enjoy creating online content very much; 2) Creating online content doesn t interest you at all; and 3) You participate online for the pleasure you experience when you do it. 11 The demographic profile of the sample of the small town university was 79% white, 11% Latino, 4% Asian, 3% African American, 0.4% Native American or Alaskan Native, 3% other, and the actual university population is 76% white, 13% Latino, 5% Asian, 3% African American, 0.5% Native American or Alaskan Native, and 2% other. In the case of the metropolitan-area university, the demographic profile of the sample was 38% white, 26% Asian, 20% Latino, 9% African American, 0.3% Native American or Alaskan native, and 5% other, and the actual demographic breakdown is 36% white, 20% Asian, 20% Hispanic, 14% African American, 0.3% Native American, and 11% other. 12 Formal multicollinearity tests were. The results show that the highest variance inflation factor (VIF) associated with a variable was 1.9, for intrinsic motivation. Given the large sample size and the statistical power provided by such a sample, this level of multicollinearity is not problematic (Hayes, 2005). The condition index, which measures how dependent one predictor variable is on another, is 24. This figure is under 30, the threshold suggested by Tabachnick and Fidell (2001) to determine the presence of multicollinearity. 13 A more detailed analysis revealed that men contributed with online content more frequently than women in six out of ten activities. Females created more digital content than males in three out of 10 activities. Only the difference in one activity (i.e., post comments on blogs) was not statistically significant. 14 A detailed comparison revealed that among all racial groups, white students created online content less frequently than all the other groups 8 out of 10 activities. The only exceptions were use of social networking sites and creation of blogs, where whites ranked second after Asian students. References Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall. Bandura, A., & Cervone, D. (1983). Self-evaluative and self-efficacy mechanisms governing the motivational effects of goal systems. Journal of Personality and Social Psychology, 45, Broos, A., & Roe, K. (2006). The digital divide in the playstation generation: Self-efficacy, locus of control and ICT adoption among adolescents. Poetics, 34(4 5), Bucy, E. P. (2000). Social access to the Internet. Harvard International Journal of Press/Politics, 5(1), Carlson, C., Uppal, S., & Prosser, E. C. (2000). Ethnic differences in processes contributing to the self-esteem of early adolescent girls. Journal of Early Adolescence, 20(1), Journal of Computer-Mediated Communication 16 (2010) International Communication Association

19 Chen, H.-T. (2007). Sharing, connection, and creation in the web 2.0 era: Profiling the adopters of video-sharing and social-networking sites. Paper presented at the Association for Education in Journalism and Mass Communication, Washington D.C. Cho, J., Gil de Zuniga, H., Rojas, H., & Shah, D. V. (2003). Beyond access: The digital divide and internet uses and gratifications. IT & Society, 1(4), Cooper, J. (2006). The digital divide: The special case of gender. Journal of Computer Assisted Learning, 22, Correa, T., Willard Hinsley, A., & Gil de Zuniga, H. (2010). Who interacts on the web? The intersection of users personality and social media use. Computers in Human Behavior, 26(2), Csikszentmihalyi, M. (1991). Flow: The psychology of optimal experience. New York: Harper & Row Publishers. Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), Davis, F., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22, de Haan, J. (2004). A multifaceted dynamic model of the digital divide. IT & Society, 1(7), Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. R. (1994). Facilitating internalization: The self-determination theory perspective. Journal of Personality, 62, Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Dholakia, R. R., Dholakia, N., & Kshetri, N. (2004). Gender and Internet usage. In H. Bidgoli (Ed.), The Internet Encyclopedia (Vol. 2, pp ). New York: John Wiley. Dillman, D. A. (2007). Mail and internet surveys: The tailored design method (2nd ed.). New York: Wiley. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide [Electronic Version]. Journal of Computer-Mediated Communication, 6. Retrieved September 28, 2008 from Elo, I. T., & Preston, S. H. (1996). Educational differentials in mortality: United states, Social Science & Medicine, 42(1), Fox, S., & Madden, M. (2005). Generations online. Pew Internet & American Life Project. Goudas, M., Biddle, S., & Martin, U. (1995). A prospective study of the relationships between motivational orientations and perceived competence with intrinsic motivation and achievement in a teacher education course. Educational Psychology, 15(1), Gatignon, H. A., & Roberston, T. S. (1991). A propositional inventory for new diffussion research. In H. H. Kassarjian & T. S. Robertson (Eds.), Perspectives in consumer behavior (4th ed., pp ). Upper Saddle River, NJ: Prentice Hall. Harackiewicz, J. M., & Larson, J. R. (1986). Managing motivation: The impact of supervisor feedback on subordinate task interest Journal of Personality and Social Psychology, 51( ). Hargittai, E. (2002). Second-level digital divide: Differences in people s online skills [Electronic Version]. First Monday, 7. Retrieved October 10, 2008 from firstmonday. org/issues/issue7 4/hargittai/. Hargittai, E. (2005). Survey measures of web-oriented digital literacy. Social Science Computer Review, 23, Journal of Computer-Mediated Communication 16 (2010) International Communication Association 89

20 Hargittai, E., & Hinnant, A. (2008). Digital inequality: Differences in young adults use of the Internet. Communication Research, 35(5), Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), Hargittai, E., & Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age. Information, Communication & Society, 11(2), Harp,D.,Bachman,I.,Loke,J.,&Cantrell,T.(forthcoming).Waveofhope:African Americans use media and engage more civically, politically than whites. Howard Journal of Communication. Hayes, A. F. (2005). Statistical methods for communication science. Mahwah, NJ: Lawrence Erlbaum Associates. Hoffman, D. L., & Novak, T. P. (1998). Bridging the racial divide on the Internet. Science, 280, Howard, P. N., Rainie, L., & Jones, S. (2001). Days and nights on the Internet: The impact of diffusing technology. American Behavioral Scientist, 45(3), Jones, S., & Fox, S. (2009). Generations online in 2009: Pew Internet & American Life Project. Jones, S., Johnson-Yale, C., Millermaier, S., & Seoane Pérez, F. (2009). Us college students Internet use: Race, gender and digital divides. Journal of Computer-Mediated Communication, 14, Katz, J., & Aspden, P. (1997). Motivations for and barriers to internet usage: Results of a national public opinion survey. Internet Research: Electronic Networking Applications and Policy, 7(3). Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), Lenhart, A. (2009). Adults and social network websites: Pew Internet & American Life Project. Lenhart, A., Madden, M., Rankin Mcgill, A., & Smith, A. (2007). Teens and social media: The use of social media gains a greater foothold in teen life as they embrace the conversational nature of interactive online media. Retrieved November 22, 2008, from pewinternet.org/pdfs/pip Teens Social Media Final.pdf Livingstone, S. (2004). Media literacy and the challenge of new information and communication technologies. Communication Review,7,3 14. Livingstone, S., & Helsper, E. (2007). Gradations in digital inclusion: Children, young people and the digital divide. New Media and Society 9(4), Loges, W. E., & Jung, J.-Y. (2001). Exploring the digital divide: Internet connectedness and age. Communication Research, 28(4), Losier, G. F., & Vallerand, R. J. (1994). The temporal relationship between perceived competence and self-determined motivation. Journal of Social Psychology, 134(6), Madden, M. (2003). America s online pursuits. Washington, D.C.: Pew Internet & American Life Project. Madden, M., & Fox, S. (2006). Riding the waves of Web 2.0 : More than a buzzword but still not easily defined. Retrieved November 22, 2008, from org/pdfs/pip Web 2.0.pdf Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88(2), Journal of Computer-Mediated Communication 16 (2010) International Communication Association

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